# import libraries
import tensorflow as tf
import os
import pathlib
import time
import datetime
from matplotlib import pyplot as plt
from IPython import display
# matplotlib stylings
plt.rcParams['figure.figsize'] = 16, 9
In order to demonstrate the prowess of image to image translation, I will be using the cityscape dataset sourced from UC Berkeley repository. The dataset comes as a set of real images and a semantic segmented version of it, with urban streets as the main focus. The objective of the GAN model is to translate a semantic segmented image into a realistic urban street image.

dataset_name = "cityscapes"
_URL = f'http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/{dataset_name}.tar.gz'
path_to_zip = tf.keras.utils.get_file(
fname=f"{dataset_name}.tar.gz",
origin=_URL,
extract=True)
path_to_zip = pathlib.Path(path_to_zip)
PATH = path_to_zip.parent/dataset_name
Downloading data from http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/cityscapes.tar.gz 103448576/103441232 [==============================] - 50s 0us/step 103456768/103441232 [==============================] - 50s 0us/step
list(PATH.parent.iterdir())
[PosixPath('/root/.keras/datasets/cityscapes.tar.gz'),
PosixPath('/root/.keras/datasets/cityscapes')]
sample_image = tf.io.read_file(str(PATH / 'train/1.jpg'))
sample_image = tf.io.decode_jpeg(sample_image)
print(sample_image.shape)
(256, 512, 3)
Each original image is of size 256 by 512, containing two images of 256 by 256.
plt.figure()
plt.imshow(sample_image)
plt.show()
def load(image_file):
# Read and decode an image file to a uint8 tensor
image = tf.io.read_file(image_file)
image = tf.io.decode_jpeg(image)
# Split each image tensor into two tensors:
# - one with a real building facade image
# - one with an architecture label image
w = tf.shape(image)[1]
w = w // 2
input_image = image[:, w:, :]
real_image = image[:, :w, :]
# Convert both images to float32 tensors
input_image = tf.cast(input_image, tf.float32)
real_image = tf.cast(real_image, tf.float32)
return input_image, real_image
inp, re = load(str(PATH / 'train/100.jpg'))
# Casting to int for matplotlib to display the images
fig, (ax1, ax2) = plt.subplots(1, 2)
ax1.imshow(inp / 255.0)
ax2.imshow(re / 255.0)
plt.show()
According to the pix2pix paper, some augmentation is done to preprocess the training set, these comes as random jittering and mirroring.
# The facade training set consist of 400 images
BUFFER_SIZE = 400
# The batch size of 1 produced better results for the U-Net in the original pix2pix experiment
BATCH_SIZE = 1
# Each image is 256x256 in size
IMG_WIDTH = 256
IMG_HEIGHT = 256
def resize(input_image, real_image, height, width):
input_image = tf.image.resize(input_image, [height, width], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
real_image = tf.image.resize(real_image, [height, width], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
return input_image, real_image
def random_crop(input_image, real_image):
stacked_image = tf.stack([input_image, real_image], axis=0)
cropped_image = tf.image.random_crop(stacked_image, size=[2, IMG_HEIGHT, IMG_WIDTH, 3])
return cropped_image[0], cropped_image[1]
# Normalizing the images to [-1, 1]
def normalize(input_image, real_image):
input_image = (input_image / 127.5) - 1
real_image = (real_image / 127.5) - 1
return input_image, real_image
@tf.function()
def random_jitter(input_image, real_image):
# Resizing to 286x286
input_image, real_image = resize(input_image, real_image, 286, 286)
# Random cropping back to 256x256
input_image, real_image = random_crop(input_image, real_image)
if tf.random.uniform(()) > 0.5:
# Random mirroring
input_image = tf.image.flip_left_right(input_image)
real_image = tf.image.flip_left_right(real_image)
return input_image, real_image
plt.figure(figsize=(6, 6))
for i in range(4):
rj_inp, rj_re = random_jitter(inp, re)
plt.subplot(2, 2, i + 1)
plt.imshow(rj_inp / 255.0)
plt.axis('off')
plt.show()
def load_image_train(image_file):
input_image, real_image = load(image_file)
input_image, real_image = random_jitter(input_image, real_image)
input_image, real_image = normalize(input_image, real_image)
return input_image, real_image
def load_image_test(image_file):
input_image, real_image = load(image_file)
input_image, real_image = resize(input_image, real_image,
IMG_HEIGHT, IMG_WIDTH)
input_image, real_image = normalize(input_image, real_image)
return input_image, real_image
# building the training dataset
train_dataset = tf.data.Dataset.list_files(str(PATH / 'train/*.jpg'))
train_dataset = train_dataset.map(load_image_train, num_parallel_calls=tf.data.AUTOTUNE)
train_dataset = train_dataset.shuffle(BUFFER_SIZE)
train_dataset = train_dataset.batch(BATCH_SIZE)
# building the testing dataset
try:
test_dataset = tf.data.Dataset.list_files(str(PATH / 'test/*.jpg'))
except tf.errors.InvalidArgumentError:
test_dataset = tf.data.Dataset.list_files(str(PATH / 'val/*.jpg'))
test_dataset = test_dataset.map(load_image_test)
test_dataset = test_dataset.batch(BATCH_SIZE)
OUTPUT_CHANNELS = 3
def downsample(filters, size, apply_batchnorm=True):
initializer = tf.random_normal_initializer(0., 0.02)
result = tf.keras.Sequential()
result.add(
tf.keras.layers.Conv2D(filters, size, strides=2, padding='same',
kernel_initializer=initializer, use_bias=False))
if apply_batchnorm:
result.add(tf.keras.layers.BatchNormalization())
result.add(tf.keras.layers.LeakyReLU())
return result
def upsample(filters, size, apply_dropout=False):
# Gaussian Weight Initialization
initializer = tf.random_normal_initializer(0., 0.02)
result = tf.keras.Sequential()
result.add(
tf.keras.layers.Conv2DTranspose(filters, size, strides=2,
padding='same',
kernel_initializer=initializer,
use_bias=False))
result.add(tf.keras.layers.BatchNormalization())
if apply_dropout:
result.add(tf.keras.layers.SpatialDropout2D(0.5))
result.add(tf.keras.layers.ReLU())
return result
def Generator():
inputs = tf.keras.layers.Input(shape=[256, 256, 3])
down_stack = [
downsample(64, 4, apply_batchnorm=False), # (batch_size, 128, 128, 64)
downsample(128, 4), # (batch_size, 64, 64, 128)
downsample(256, 4), # (batch_size, 32, 32, 256)
downsample(512, 4), # (batch_size, 16, 16, 512)
downsample(512, 4), # (batch_size, 8, 8, 512)
downsample(512, 4), # (batch_size, 4, 4, 512)
downsample(512, 4), # (batch_size, 2, 2, 512)
downsample(512, 4), # (batch_size, 1, 1, 512)
]
up_stack = [
upsample(512, 4, apply_dropout=True), # (batch_size, 2, 2, 1024)
upsample(512, 4, apply_dropout=True), # (batch_size, 4, 4, 1024)
upsample(512, 4, apply_dropout=True), # (batch_size, 8, 8, 1024)
upsample(512, 4), # (batch_size, 16, 16, 1024)
upsample(256, 4), # (batch_size, 32, 32, 512)
upsample(128, 4), # (batch_size, 64, 64, 256)
upsample(64, 4), # (batch_size, 128, 128, 128)
]
initializer = tf.random_normal_initializer(0., 0.02)
last = tf.keras.layers.Conv2DTranspose(OUTPUT_CHANNELS, 4,
strides=2,
padding='same',
kernel_initializer=initializer,
activation='tanh') # (batch_size, 256, 256, 3)
x = inputs
# Downsampling through the model
skips = []
for down in down_stack:
x = down(x)
skips.append(x)
skips = reversed(skips[:-1])
# Upsampling and establishing the skip connections
for up, skip in zip(up_stack, skips):
x = up(x)
x = tf.keras.layers.Concatenate()([x, skip])
x = last(x)
return tf.keras.Model(inputs=inputs, outputs=x)
generator = Generator()
generator.summary()
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 256, 256, 3 0 []
)]
sequential (Sequential) (None, 128, 128, 64 3072 ['input_1[0][0]']
)
sequential_1 (Sequential) (None, 64, 64, 128) 131584 ['sequential[0][0]']
sequential_2 (Sequential) (None, 32, 32, 256) 525312 ['sequential_1[0][0]']
sequential_3 (Sequential) (None, 16, 16, 512) 2099200 ['sequential_2[0][0]']
sequential_4 (Sequential) (None, 8, 8, 512) 4196352 ['sequential_3[0][0]']
sequential_5 (Sequential) (None, 4, 4, 512) 4196352 ['sequential_4[0][0]']
sequential_6 (Sequential) (None, 2, 2, 512) 4196352 ['sequential_5[0][0]']
sequential_7 (Sequential) (None, 1, 1, 512) 4196352 ['sequential_6[0][0]']
sequential_8 (Sequential) (None, 2, 2, 512) 4196352 ['sequential_7[0][0]']
concatenate (Concatenate) (None, 2, 2, 1024) 0 ['sequential_8[0][0]',
'sequential_6[0][0]']
sequential_9 (Sequential) (None, 4, 4, 512) 8390656 ['concatenate[0][0]']
concatenate_1 (Concatenate) (None, 4, 4, 1024) 0 ['sequential_9[0][0]',
'sequential_5[0][0]']
sequential_10 (Sequential) (None, 8, 8, 512) 8390656 ['concatenate_1[0][0]']
concatenate_2 (Concatenate) (None, 8, 8, 1024) 0 ['sequential_10[0][0]',
'sequential_4[0][0]']
sequential_11 (Sequential) (None, 16, 16, 512) 8390656 ['concatenate_2[0][0]']
concatenate_3 (Concatenate) (None, 16, 16, 1024 0 ['sequential_11[0][0]',
) 'sequential_3[0][0]']
sequential_12 (Sequential) (None, 32, 32, 256) 4195328 ['concatenate_3[0][0]']
concatenate_4 (Concatenate) (None, 32, 32, 512) 0 ['sequential_12[0][0]',
'sequential_2[0][0]']
sequential_13 (Sequential) (None, 64, 64, 128) 1049088 ['concatenate_4[0][0]']
concatenate_5 (Concatenate) (None, 64, 64, 256) 0 ['sequential_13[0][0]',
'sequential_1[0][0]']
sequential_14 (Sequential) (None, 128, 128, 64 262400 ['concatenate_5[0][0]']
)
concatenate_6 (Concatenate) (None, 128, 128, 12 0 ['sequential_14[0][0]',
8) 'sequential[0][0]']
conv2d_transpose_7 (Conv2DTran (None, 256, 256, 3) 6147 ['concatenate_6[0][0]']
spose)
==================================================================================================
Total params: 54,425,859
Trainable params: 54,414,979
Non-trainable params: 10,880
__________________________________________________________________________________________________
tf.keras.utils.plot_model(generator, show_shapes=True, dpi=64)
LAMBDA = 100
loss_object = tf.keras.losses.BinaryCrossentropy(from_logits=True)
def generator_loss(disc_generated_output, gen_output, target):
gan_loss = loss_object(tf.ones_like(disc_generated_output), disc_generated_output)
# Mean absolute error
l1_loss = tf.reduce_mean(tf.abs(target - gen_output))
total_gen_loss = gan_loss + (LAMBDA * l1_loss)
return total_gen_loss, gan_loss, l1_loss
def Discriminator():
initializer = tf.random_normal_initializer(0., 0.02)
inp = tf.keras.layers.Input(shape=[256, 256, 3], name='input_image')
tar = tf.keras.layers.Input(shape=[256, 256, 3], name='target_image')
x = tf.keras.layers.concatenate([inp, tar]) # (batch_size, 256, 256, channels*2)
down1 = downsample(64, 4, False)(x) # (batch_size, 128, 128, 64)
down2 = downsample(128, 4)(down1) # (batch_size, 64, 64, 128)
down3 = downsample(256, 4)(down2) # (batch_size, 32, 32, 256)
zero_pad1 = tf.keras.layers.ZeroPadding2D()(down3) # (batch_size, 34, 34, 256)
conv = tf.keras.layers.Conv2D(512, 4, strides=1,
kernel_initializer=initializer,
use_bias=False)(zero_pad1) # (batch_size, 31, 31, 512)
batchnorm1 = tf.keras.layers.BatchNormalization()(conv)
leaky_relu = tf.keras.layers.LeakyReLU()(batchnorm1)
zero_pad2 = tf.keras.layers.ZeroPadding2D()(leaky_relu) # (batch_size, 33, 33, 512)
last = tf.keras.layers.Conv2D(1, 4, strides=1,
kernel_initializer=initializer)(zero_pad2) # (batch_size, 30, 30, 1)
return tf.keras.Model(inputs=[inp, tar], outputs=last)
discriminator = Discriminator()
discriminator.summary()
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_image (InputLayer) [(None, 256, 256, 3 0 []
)]
target_image (InputLayer) [(None, 256, 256, 3 0 []
)]
concatenate_7 (Concatenate) (None, 256, 256, 6) 0 ['input_image[0][0]',
'target_image[0][0]']
sequential_15 (Sequential) (None, 128, 128, 64 6144 ['concatenate_7[0][0]']
)
sequential_16 (Sequential) (None, 64, 64, 128) 131584 ['sequential_15[0][0]']
sequential_17 (Sequential) (None, 32, 32, 256) 525312 ['sequential_16[0][0]']
zero_padding2d (ZeroPadding2D) (None, 34, 34, 256) 0 ['sequential_17[0][0]']
conv2d_11 (Conv2D) (None, 31, 31, 512) 2097152 ['zero_padding2d[0][0]']
batch_normalization_16 (BatchN (None, 31, 31, 512) 2048 ['conv2d_11[0][0]']
ormalization)
leaky_re_lu_11 (LeakyReLU) (None, 31, 31, 512) 0 ['batch_normalization_16[0][0]']
zero_padding2d_1 (ZeroPadding2 (None, 33, 33, 512) 0 ['leaky_re_lu_11[0][0]']
D)
conv2d_12 (Conv2D) (None, 30, 30, 1) 8193 ['zero_padding2d_1[0][0]']
==================================================================================================
Total params: 2,770,433
Trainable params: 2,768,641
Non-trainable params: 1,792
__________________________________________________________________________________________________
tf.keras.utils.plot_model(discriminator, show_shapes=True, dpi=64)
def discriminator_loss(disc_real_output, disc_generated_output):
real_loss = loss_object(tf.ones_like(disc_real_output), disc_real_output)
generated_loss = loss_object(tf.zeros_like(disc_generated_output), disc_generated_output)
total_disc_loss = real_loss + generated_loss
return total_disc_loss
generator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
discriminator_optimizer = tf.keras.optimizers.Adam(2e-4, beta_1=0.5)
checkpoint_dir = './training_checkpoints'
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt")
checkpoint = tf.train.Checkpoint(generator_optimizer=generator_optimizer,
discriminator_optimizer=discriminator_optimizer,
generator=generator,
discriminator=discriminator)
def generate_images(model, test_input, tar):
prediction = model(test_input, training=True)
plt.figure(figsize=(15, 15))
display_list = [test_input[0], tar[0], prediction[0]]
title = ['Input Image', 'Ground Truth', 'Predicted Image']
for i in range(3):
plt.subplot(1, 3, i+1)
plt.title(title[i])
# Getting the pixel values in the [0, 1] range to plot.
plt.imshow(display_list[i] * 0.5 + 0.5)
plt.axis('off')
plt.show()
for example_input, example_target in test_dataset.take(1):
generate_images(generator, example_input, example_target)
log_dir="logs/"
summary_writer = tf.summary.create_file_writer(log_dir + "fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
@tf.function
def train_step(input_image, target, step):
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
gen_output = generator(input_image, training=True)
disc_real_output = discriminator([input_image, target], training=True)
disc_generated_output = discriminator([input_image, gen_output], training=True)
gen_total_loss, gen_gan_loss, gen_l1_loss = generator_loss(disc_generated_output, gen_output, target)
disc_loss = discriminator_loss(disc_real_output, disc_generated_output)
# computes gradient over loss function
generator_gradients = gen_tape.gradient(gen_total_loss,
generator.trainable_variables)
discriminator_gradients = disc_tape.gradient(disc_loss,
discriminator.trainable_variables)
# update gradients
generator_optimizer.apply_gradients(zip(generator_gradients,
generator.trainable_variables))
discriminator_optimizer.apply_gradients(zip(discriminator_gradients,
discriminator.trainable_variables))
with summary_writer.as_default():
tf.summary.scalar('gen_total_loss', gen_total_loss, step=step//1000)
tf.summary.scalar('gen_gan_loss', gen_gan_loss, step=step//1000)
tf.summary.scalar('gen_l1_loss', gen_l1_loss, step=step//1000)
tf.summary.scalar('disc_loss', disc_loss, step=step//1000)
def fit(train_ds, test_ds, steps):
example_input, example_target = next(iter(test_ds.take(1)))
start = time.time()
for step, (input_image, target) in train_ds.repeat().take(steps).enumerate():
if (step) % 1000 == 0:
# display.clear_output(wait=True)
if step != 0:
print(f'Time taken for 1000 steps: {time.time()-start:.2f} sec\n')
start = time.time()
generate_images(generator, example_input, example_target)
print(f"Step: {step//1000}k")
train_step(input_image, target, step)
# Training step
if (step+1) % 10 == 0:
print('.', end='', flush=True)
# Save (checkpoint) the model every 5k steps
if (step + 1) % 5000 == 0:
checkpoint.save(file_prefix=checkpoint_prefix)
%load_ext tensorboard
%tensorboard --logdir {log_dir}
%%time
fit(train_dataset, test_dataset, steps=50000)
Step: 0k ....................................................................................................Time taken for 1000 steps: 53.96 sec
Step: 1k ....................................................................................................Time taken for 1000 steps: 50.88 sec
Step: 2k ....................................................................................................Time taken for 1000 steps: 51.62 sec
Step: 3k ....................................................................................................Time taken for 1000 steps: 50.83 sec
Step: 4k ....................................................................................................Time taken for 1000 steps: 52.70 sec
Step: 5k ....................................................................................................Time taken for 1000 steps: 51.57 sec
Step: 6k ....................................................................................................Time taken for 1000 steps: 50.87 sec
Step: 7k ....................................................................................................Time taken for 1000 steps: 50.90 sec
Step: 8k ....................................................................................................Time taken for 1000 steps: 51.65 sec
Step: 9k ....................................................................................................Time taken for 1000 steps: 52.46 sec
Step: 10k ....................................................................................................Time taken for 1000 steps: 50.89 sec
Step: 11k ....................................................................................................Time taken for 1000 steps: 51.42 sec
Step: 12k ....................................................................................................Time taken for 1000 steps: 50.85 sec
Step: 13k ....................................................................................................Time taken for 1000 steps: 50.96 sec
Step: 14k ....................................................................................................Time taken for 1000 steps: 53.10 sec
Step: 15k ....................................................................................................Time taken for 1000 steps: 50.75 sec
Step: 16k ....................................................................................................Time taken for 1000 steps: 50.83 sec
Step: 17k ....................................................................................................Time taken for 1000 steps: 51.54 sec
Step: 18k ....................................................................................................Time taken for 1000 steps: 50.82 sec
Step: 19k ....................................................................................................Time taken for 1000 steps: 52.61 sec
Step: 20k ....................................................................................................Time taken for 1000 steps: 51.58 sec
Step: 21k ....................................................................................................Time taken for 1000 steps: 50.88 sec
Step: 22k ....................................................................................................Time taken for 1000 steps: 50.81 sec
Step: 23k ....................................................................................................Time taken for 1000 steps: 51.68 sec
Step: 24k ....................................................................................................Time taken for 1000 steps: 52.28 sec
Step: 25k ....................................................................................................Time taken for 1000 steps: 50.85 sec
Step: 26k ....................................................................................................Time taken for 1000 steps: 51.45 sec
Step: 27k ....................................................................................................Time taken for 1000 steps: 50.86 sec
Step: 28k ....................................................................................................Time taken for 1000 steps: 50.79 sec
Step: 29k ....................................................................................................Time taken for 1000 steps: 53.04 sec
Step: 30k ....................................................................................................Time taken for 1000 steps: 50.87 sec
Step: 31k ....................................................................................................Time taken for 1000 steps: 50.81 sec
Step: 32k ....................................................................................................Time taken for 1000 steps: 51.66 sec
Step: 33k ....................................................................................................Time taken for 1000 steps: 50.93 sec
Step: 34k ....................................................................................................Time taken for 1000 steps: 52.56 sec
Step: 35k ....................................................................................................Time taken for 1000 steps: 51.59 sec
Step: 36k ....................................................................................................Time taken for 1000 steps: 50.85 sec
Step: 37k ....................................................................................................Time taken for 1000 steps: 50.90 sec
Step: 38k ....................................................................................................Time taken for 1000 steps: 51.74 sec
Step: 39k ....................................................................................................Time taken for 1000 steps: 52.16 sec
Step: 40k ....................................................................................................Time taken for 1000 steps: 50.90 sec
Step: 41k ....................................................................................................Time taken for 1000 steps: 51.61 sec
Step: 42k ....................................................................................................Time taken for 1000 steps: 50.91 sec
Step: 43k ....................................................................................................Time taken for 1000 steps: 50.90 sec
Step: 44k ....................................................................................................Time taken for 1000 steps: 53.30 sec
Step: 45k ....................................................................................................Time taken for 1000 steps: 50.94 sec
Step: 46k ....................................................................................................Time taken for 1000 steps: 50.93 sec
Step: 47k ....................................................................................................Time taken for 1000 steps: 51.80 sec
Step: 48k ....................................................................................................Time taken for 1000 steps: 50.98 sec
Step: 49k ....................................................................................................CPU times: user 27min 50s, sys: 1min 41s, total: 29min 31s Wall time: 42min 55s
display.IFrame(
src="https://tensorboard.dev/experiment/lZ0C6FONROaUMfjYkVyJqw",
width="100%",
height="1000px")
!ls {checkpoint_dir}
checkpoint ckpt-5.data-00000-of-00001 ckpt-10.data-00000-of-00001 ckpt-5.index ckpt-10.index ckpt-6.data-00000-of-00001 ckpt-1.data-00000-of-00001 ckpt-6.index ckpt-1.index ckpt-7.data-00000-of-00001 ckpt-2.data-00000-of-00001 ckpt-7.index ckpt-2.index ckpt-8.data-00000-of-00001 ckpt-3.data-00000-of-00001 ckpt-8.index ckpt-3.index ckpt-9.data-00000-of-00001 ckpt-4.data-00000-of-00001 ckpt-9.index ckpt-4.index
# Restoring the latest checkpoint in checkpoint_dir
checkpoint.restore(tf.train.latest_checkpoint(checkpoint_dir))
<tensorflow.python.training.tracking.util.CheckpointLoadStatus at 0x7fb72bcc7390>
# Run the trained model on a few examples from the test set
for inp, tar in test_dataset.take(15):
generate_images(generator, inp, tar)